How to Visualize PhantomBuster Data in Python with pandas

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build PhantomBuster-connected Python applications and scripts for visualizing PhantomBuster data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to PhantomBuster data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live PhantomBuster data in Python. When you issue complex SQL queries from PhantomBuster, the driver pushes supported SQL operations, like filters and aggregations, directly to PhantomBuster and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to PhantomBuster Data

Connecting to PhantomBuster data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Using API Key Authentication

To use the Phantombuster API, you need to obtain an API key from your Phantombuster account settings. Navigate to phantombuster.com, click your profile icon, select Settings, and copy the API key from the API section.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Phantombuster API key from the account settings page.

Multi-Organization Accounts

If your API key is associated with multiple organizations, you can target a specific organization by setting the OrganizationId connection property to the desired organization identifier. When set, it is sent as the X-Phantombuster-Org request header.

Example connection string:

Profile=C:\profiles\Phantombuster.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key_here"

Follow the procedure below to install the required modules and start accessing PhantomBuster through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize PhantomBuster Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with PhantomBuster data.

engine = create_engine("api:///?Profile=C:\profiles\Phantombuster.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_api_key_here"")

Execute SQL to PhantomBuster

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT , FROM Agents WHERE = ''", engine)

Visualize PhantomBuster Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the PhantomBuster data. The show method displays the chart in a new window.

df.plot(kind="bar", x="", y="")
plt.show()

👁 PhantomBuster data in a Python plot (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to PhantomBuster data. Reach out to our Support Team if you have any questions.

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Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("api:///?Profile=C:\profiles\Phantombuster.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_api_key_here"")
df = pandas.read_sql("SELECT , FROM Agents WHERE = ''", engine)

df.plot(kind="bar", x="", y="")
plt.show()